Treffer: Molecular Dynamics Simulations and Their Novel Applications in Drug Delivery for Cancer Treatment: A Review.
Title:
Molecular Dynamics Simulations and Their Novel Applications in Drug Delivery for Cancer Treatment: A Review.
Authors:
Sarac B; Department of Biomedical Engineering, Faculty of Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey.; Department of Biomedical Engineering, BioriginAI Research Group, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey.; Biomedical Electronic Design Application and Research Center (BETAM), Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey., Yücer S; Department of Biomedical Engineering, Faculty of Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey.; Department of Biomedical Engineering, BioriginAI Research Group, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey.; Biomedical Electronic Design Application and Research Center (BETAM), Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey., Ciftci F; Department of Biomedical Engineering, Faculty of Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey. fciftci@fsm.edu.tr.; Department of Biomedical Engineering, BioriginAI Research Group, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Turkey. fciftci@fsm.edu.tr.; Biomedical Electronic Design Application and Research Center (BETAM), Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey. fciftci@fsm.edu.tr., Ghorbanpour M; Department of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran., Ozerol EA; Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Istanbul, Turkey.
Source:
Annals of biomedical engineering [Ann Biomed Eng] 2025 Dec; Vol. 53 (12), pp. 3254-3284. Date of Electronic Publication: 2025 Sep 29.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 0361512 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-9686 (Electronic) Linking ISSN: 00906964 NLM ISO Abbreviation: Ann Biomed Eng Subsets: MEDLINE
Imprint Name(s):
Publication: 2005- : New York : Springer Science + Business Media
Original Publication: New York, Academic Press.
Original Publication: New York, Academic Press.
MeSH Terms:
References:
Mottini, C., F. Napolitano, Z. Li, X. Gao, and L. Cardone. Computer-aided drug repurposing for cancer therapy: approaches and opportunities to challenge anticancer targets. Semin. Cancer Biol. 2021. https://doi.org/10.1016/j.semcancer.2019.09.023 . (PMID: 10.1016/j.semcancer.2019.09.02331562957)
Jeevanandam, J., K. X. Tan, M. K. Danquah, H. Guo, and A. Turgeson. Advancing aptamers as molecular probes for cancer theranostic applications—the role of molecular dynamics simulation. Biotechnol. J. 15:1900368, 2020. https://doi.org/10.1002/BIOT.201900368 . (PMID: 10.1002/BIOT.201900368)
Li, X., F. Miao, R. Xin, Z. Tai, H. Pan, H. Huang, J. Yu, Z. Chen, and Q. Zhu. Combining network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification to examine the efficacy and immunoregulation mechanism of FHB granules on vitiligo. Front. Immunol. 2023. https://doi.org/10.3389/fimmu.2023.1194823 . (PMID: 10.3389/fimmu.2023.11948233839039710777728)
Lopes, D., S. Jakobtorweihen, C. Nunes, B. Sarmento, and S. Reis. Shedding light on the puzzle of drug-membrane interactions: experimental techniques and molecular dynamics simulations. Prog. Lipid Res. 2017. https://doi.org/10.1016/j.plipres.2016.12.001 . (PMID: 10.1016/j.plipres.2016.12.00127939295)
Xu, X., A. Liu, S. Liu, Y. Ma, X. Zhang, M. Zhang, J. Zhao, S. Sun, and X. Sun. Application of molecular dynamics simulation in self-assembled cancer nanomedicine. Biomater. Res. 27:1–32, 2023. https://doi.org/10.1186/s40824-023-00386-7 . (PMID: 10.1186/s40824-023-00386-7)
Barbhuiya, S., and B. B. Das. Molecular dynamics simulation in concrete research: a systematic review of techniques, models and future directions. J. Build. Eng. 2023. https://doi.org/10.1016/j.jobe.2023.107267 . (PMID: 10.1016/j.jobe.2023.107267)
Borhani, D. W., and D. E. Shaw. The future of molecular dynamics simulations in drug discovery. J. Comput. Aided. Mol. Des. 26:15–26, 2012. https://doi.org/10.1007/s10822-011-9517-y . (PMID: 10.1007/s10822-011-9517-y22183577)
Durrant, J. D., and J. A. McCammon. Molecular dynamics simulations and drug discovery. BMC Biol. 2011. https://doi.org/10.1186/1741-7007-9-71 . (PMID: 10.1186/1741-7007-9-71220354603203851)
Hollingsworth, S. A., and R. O. Dror. Molecular dynamics simulation for all. Neuron. 99:1129, 2018. https://doi.org/10.1016/J.NEURON.2018.08.011 . (PMID: 10.1016/J.NEURON.2018.08.011302362836209097)
Bunker, A., and T. Róg. Mechanistic understanding from molecular dynamics simulation in pharmaceutical research 1: drug delivery. Front. Mol. Biosci. 2020. https://doi.org/10.3389/fmolb.2020.604770 . (PMID: 10.3389/fmolb.2020.604770333306337732618)
Duran, T., A. Costa, A. Gupta, X. Xu, H. Zhang, D. Burgess, and B. Chaudhuri. Coarse-grained molecular dynamics simulations of paclitaxel-loaded polymeric micelles. Mol. Pharm. 19:1117–1134, 2022. https://doi.org/10.1021/acs.molpharmaceut.1c00800 . (PMID: 10.1021/acs.molpharmaceut.1c0080035243863)
Ashwini, T., R. Narayan, P. A. Shenoy, and U. Y. Nayak. Computational modeling for the design and development of nano based drug delivery systems. J. Mol. Liq. 2022. https://doi.org/10.1016/j.molliq.2022.120596 . (PMID: 10.1016/j.molliq.2022.120596)
Farhadian, N., M. S. Kazemi, F. Moosavi Baigi, and M. Khalaj. Molecular dynamics simulation of drug delivery across the cell membrane by applying gold nanoparticle carrier: flutamide as hydrophobic and glutathione as hydrophilic drugs as the case studies. J. Mol. Graph. Model. 2022. https://doi.org/10.1016/j.jmgm.2022.108271 . (PMID: 10.1016/j.jmgm.2022.10827135863117)
Ivánczi, M., B. Balogh, L. Kis, and I. Mándity. Molecular dynamics simulations of drug-conjugated cell-penetrating peptides. Pharmaceuticals. 2023. https://doi.org/10.3390/ph16091251 . (PMID: 10.3390/ph160912513776505910535489)
Martinotti, C., L. Ruiz-Perez, E. Deplazes, and R. L. Mancera. Molecular dynamics simulation of small molecules interacting with biological membranes. ChemPhysChem. 2020. https://doi.org/10.1002/cphc.202000219 . (PMID: 10.1002/cphc.20200021932452115)
Badar, M. S., S. Shamsi, J. Ahmed, and M. A. Alam. Molecular Dynamics Simulations: Concept, Methods, and Applications. Springer Nature, pp. 131–151, 2022.
Hénin, J., T. Lelièvre, M. R. Shirts, O. Valsson, and L. Delemotte. Enhanced sampling methods for molecular dynamics simulations [Article v1.0]. Living J. Comput. Mol. Sci. 2022. https://doi.org/10.33011/livecoms.4.1.1583 . (PMID: 10.33011/livecoms.4.1.1583)
Singh, S., and V. K. Singh. Molecular dynamics simulation: methods and application. In: Frontiers in Protein Structure, Function, and Dynamics, Springer, 2020, pp. 213–238. (PMID: 10.1007/978-981-15-5530-5_9)
Lee, H. S., Y. Qi, and W. Im. Effects of N-glycosylation on protein conformation and dynamics: Protein Data Bank analysis and molecular dynamics simulation study. Sci. Rep. 2015. https://doi.org/10.1038/srep08926 . (PMID: 10.1038/srep08926266919294686891)
Shahapure, N., D. Shinde, and A. Kelkar. Atomistic modeling and molecular dynamic simulation of polymer nanocomposites for thermal and mechanical property characterization: a review. AIMS Mater. Sci. 10:249–287, 2023. https://doi.org/10.3934/matersci.2023014 . (PMID: 10.3934/matersci.2023014)
Swai, R. E. A review of molecular dynamics simulations in the designing of effective shale inhibitors: application for drilling with water-based drilling fluids. J. Pet. Explor. Prod. Technol. 2020. https://doi.org/10.1007/s13202-020-01003-2 . (PMID: 10.1007/s13202-020-01003-2)
Pawnikar, S., A. Bhattarai, J. Wang, and Y. Miao. Binding analysis using accelerated molecular dynamics simulations and future perspectives. Adv. Appl. Bioinforma. Chem. 15:1–19, 2022. https://doi.org/10.2147/AABC.S247950 . (PMID: 10.2147/AABC.S247950)
Lindorff-Larsen, K., P. Maragakis, S. Piana, M. P. Eastwood, R. O. Dror, and D. E. Shaw. Systematic validation of protein force fields against experimental data. PLoS One. 7:e32131, 2012. https://doi.org/10.1371/JOURNAL.PONE.0032131 . (PMID: 10.1371/JOURNAL.PONE.0032131223841573285199)
Senn, H. M., and W. Thiel. QM/MM methods for biomolecular systems. Angew. Chemie—Int. Ed. 2009. https://doi.org/10.1002/anie.200802019 . (PMID: 10.1002/anie.200802019)
Sapay, N., and D. P. Tieleman. Chapter 4 Molecular dynamics simulation of lipid-protein interactions. Curr. Top. Membr. 2008. https://doi.org/10.1016/S1063-5823(08)00004-5 . (PMID: 10.1016/S1063-5823(08)00004-5)
Wang, Y., C. B. Harrison, K. Schulten, and J. A. McCammon. Implementation of accelerated molecular dynamics in NAMD. Comput. Sci. Discov. 2011. https://doi.org/10.1088/1749-4699/4/1/015002 . (PMID: 10.1088/1749-4699/4/1/015002216860633115733)
Al-Akhras, M. A., B. Odat, V. Narayanaswamy, M. S. Mousa, B. Issa, M. Obeidat, I. A. Al-Omari, and I. M. Obaidat. Molecular simulations of 6-gingerol loading on graphene and graphene oxide for drug delivery applications. Biointerface Res. Appl. Chem. 2023. https://doi.org/10.33263/BRIAC133.258 . (PMID: 10.33263/BRIAC133.258)
Arslan, M. B. Comparative analysis of mitoxantrone and doxorubicin interactions with single-walled carbon nanotubes using molecular dynamics simulations. El-Cezeri J. Sci. Eng. 10:656–666, 2023. https://doi.org/10.31202/ecjse.1345238 . (PMID: 10.31202/ecjse.1345238)
Gocheva, G., and A. Ivanova. A look at receptor-ligand pairs for active-targeting drug delivery from crystallographic and molecular dynamics perspectives. Mol. Pharm. 16:3293–3321, 2019. https://doi.org/10.1021/acs.molpharmaceut.9b00250 . (PMID: 10.1021/acs.molpharmaceut.9b0025031274322)
Liang, L., J. W. Shen, and Q. Wang. Molecular dynamics study on DNA nanotubes as drug delivery vehicle for anticancer drugs. Colloids Surfaces B Biointerfaces. 153:168–173, 2017. https://doi.org/10.1016/j.colsurfb.2017.02.021 . (PMID: 10.1016/j.colsurfb.2017.02.02128237820)
Lee, H. Molecular simulations of pegylated biomolecules, liposomes, and nanoparticles for drug delivery applications. Pharmaceutics. 2020. https://doi.org/10.3390/pharmaceutics12060533 . (PMID: 10.3390/pharmaceutics12060533333792957823613)
Saurabh, S., P. M. Sivakumar, V. Perumal, A. Khosravi, A. Sugumaran, and V. Prabhawathi. Molecular dynamics simulations in drug discovery and drug delivery. In: Engineering Materials, Springer, 2020, pp. 275–301.
Shariatinia, Z., 2021a. Molecular dynamics simulations on drug delivery systems, in: Modeling and Control of Drug Delivery Systems. pp. 153–182. https://doi.org/10.1016/B978-0-12-821185-4.00013-0.
Razmimanesh, F., S. Amjad-Iranagh, and H. Modarress. Molecular dynamics simulation study of chitosan and gemcitabine as a drug delivery system. J. Mol. Model. 2015. https://doi.org/10.1007/S00894-015-2705-2 . (PMID: 10.1007/S00894-015-2705-226044358)
Salahshoori, I., M. Golriz, M. A. L. Nobre, S. Mahdavi, R. Eshaghi Malekshah, A. Javdani-Mallak, M. Namayandeh Jorabchi, H. Ali Khonakdar, Q. Wang, A. H. Mohammadi, S. Masoomeh Sadat Mirnezami, and F. Kargaran. Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges. J. Mol. Liq. 2024. https://doi.org/10.1016/j.molliq.2023.123888 . (PMID: 10.1016/j.molliq.2023.123888)
Shariatinia, Z., and A. Mazloom-Jalali. Molecular dynamics simulations on chitosan/graphene nanocomposites as anticancer drug delivery using systems. Chinese J. Phys. 66:362–382, 2020. https://doi.org/10.1016/j.cjph.2020.04.012 . (PMID: 10.1016/j.cjph.2020.04.012)
Brooks, B. R., C. L. Brooks, A. D. Mackerell, L. Nilsson, R. J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York, and M. Karplus. CHARMM: the biomolecular simulation program. J. Comput. Chem. 30:1545–1614, 2009. https://doi.org/10.1002/jcc.21287 . (PMID: 10.1002/jcc.21287194448162810661)
Zeng, K., and K. Chen. Molecular dynamics simulation calculation method for elasticity and plasticity of metal nanostructures. Adv. Mater. Sci. Eng. 2022. https://doi.org/10.1155/2022/6264256 . (PMID: 10.1155/2022/6264256)
Frenkel, D., Smit, B., 2023. Understanding Molecular Simulation. https://doi.org/10.1016/c2009-0-63921-0.
Berendsen, H. J. C., and W. F. Gunsteren. Molecular dynamics simulations: techniques and approaches. In: Molecular Liquids, Springer, 1984, pp. 475–500. (PMID: 10.1007/978-94-009-6463-1_16)
Konczak, L., J. Narkiewicz-Michalek, G. Pastorin, and T. Panczyk. Effects of intermolecular interactions on the stability of carbon nanotube–gold nanoparticle conjugates in solution. Int. J. Nanomed. 11:5837–5849, 2016. https://doi.org/10.2147/IJN.S117858 . (PMID: 10.2147/IJN.S117858)
Latorraca, N. R., N. M. Fastman, A. J. Venkatakrishnan, W. B. Frommer, R. O. Dror, and L. Feng. Mechanism of substrate translocation in an alternating access transporter. Cell. 169:96-107.e12, 2017. https://doi.org/10.1016/j.cell.2017.03.010 . (PMID: 10.1016/j.cell.2017.03.010283403545557413)
Kappel, K., Y. Miao, and J. Andrew McCammon. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q. Rev. Biophys. 48:479–487, 2015. https://doi.org/10.1017/S0033583515000153 . (PMID: 10.1017/S0033583515000153265374085435230)
Shang, S., Q. Zhao, D. Zhang, R. Sun, and Y. Tang. Molecular dynamics simulation of the adsorption behavior of two different drugs on hydroxyapatite and Zn-doped hydroxyapatite. Mater. Sci. Eng. C. 105:110017, 2019. https://doi.org/10.1016/j.msec.2019.110017 . (PMID: 10.1016/j.msec.2019.110017)
Wang, Q., M. Wang, X. Lu, K. Wang, L. Fang, F. Ren, and G. Lu. Effects of atomic-level nano-structured hydroxyapatite on adsorption of bone morphogenetic protein-7 and its derived peptide by computer simulation. Sci. Rep. 7:1–14, 2017. https://doi.org/10.1038/s41598-017-15219-6 . (PMID: 10.1038/s41598-017-15219-6281270515428335)
Wang, Q., M. H. Wang, K. F. Wang, Y. Liu, H. P. Zhang, X. Lu, and X. D. Zhang. Computer simulation of biomolecule-biomaterial interactions at surfaces and interfaces. Biomed. Mater. 2015. https://doi.org/10.1088/1748-6041/10/3/032001 . (PMID: 10.1088/1748-6041/10/3/03200126694757)
Uddin, N. M., F. M. Capaldi, and B. Farouk. Molecular dynamics simulations of the interactions and dispersion of carbon nanotubes in polyethylene oxide/water systems. Polymer (Guildf). 52:288–296, 2011. https://doi.org/10.1016/j.polymer.2010.11.056 . (PMID: 10.1016/j.polymer.2010.11.056)
Rsheed, A. . Al., S. Aldawood, and O. M. Aldossary. The size and shape effects on the melting point of nanoparticles based on the lennard-jones potential function. Nanomaterials. 2021. https://doi.org/10.3390/nano11112916 . (PMID: 10.3390/nano11112916348356808621700)
Fijany, A., T. Çagin, A. Jaramillo-Botero, and W. Goddard. Novel algorithms for massively parallel, long-term, simulation of molecular dynamics systems. Adv. Eng. Softw. 1998. https://doi.org/10.1016/S0965-9978(98)00053-2 . (PMID: 10.1016/S0965-9978(98)00053-2)
Chen, K., and K. Zeng. Performance optimization model of molecular dynamics simulation based on machine learning and data mining algorithm. Mob. Inf. Syst. 2022. https://doi.org/10.1155/2022/4553446 . (PMID: 10.1155/2022/4553446)
Wu, J., W. Sang, D. Li, and L. Jin. Molecular dynamics simulation of temperature effects on sodium chloride solution adsorption in γ-FeOOH nanopores. Constr. Build. Mater. 449:138410, 2024. https://doi.org/10.1016/j.conbuildmat.2024.138410 . (PMID: 10.1016/j.conbuildmat.2024.138410)
You, D., H. Wang, W. Sun, L. Wang, H. Zhang, X. Chen, and G. Liu. Understanding the effect of temperature, concentration, and substrate material on CaCO3 scaling: molecular dynamics simulations and density functional theory. Comput. Mater. Sci. 2022. https://doi.org/10.1016/j.commatsci.2022.111352 . (PMID: 10.1016/j.commatsci.2022.111352)
Barazorda-Ccahuana, H. L., M. Nedyalkova, F. Mas, and S. Madurga. Unveiling the effect of low ph on the sars-cov-2 main protease by molecular dynamics simulations. Polymers (Basel). 2021. https://doi.org/10.3390/polym13213823 . (PMID: 10.3390/polym1321382334771379)
Lv, X., H. Liu, M. Ke, and H. Gong. Exploring the pH-dependent substrate transport mechanism of FocA using molecular dynamics simulation. Biophys. J. 2013. https://doi.org/10.1016/j.bpj.2013.11.006 . (PMID: 10.1016/j.bpj.2013.11.006243597433882453)
Mukherjee, S., M. Abdalla, M. Yadav, M. Madhavi, A. Bhrdwaj, R. Khandelwal, L. Prajapati, A. Panicker, A. Chaudhary, A. Albrakati, T. Hussain, A. Nayarisseri, and S. K. Singh. Structure-based virtual screening, molecular docking, and molecular dynamics simulation of VEGF inhibitors for the clinical treatment of ovarian cancer. J. Mol. Model. 2022. https://doi.org/10.1007/s00894-022-05081-3 . (PMID: 10.1007/s00894-022-05081-335325303)
Gopinath, P., and M. K. Kathiravan. Docking studies and molecular dynamics simulation of triazole benzene sulfonamide derivatives with human carbonic anhydrase IX inhibition activity. RSC Adv. 2021. https://doi.org/10.1039/d1ra07377j . (PMID: 10.1039/d1ra07377j354807619038044)
Shuel, S. L. Targeted cancer therapies. Can. Fam. Physician. 68:515–518, 2022. https://doi.org/10.46747/cfp.6807515 . (PMID: 10.46747/cfp.6807515358310919842142)
Hou, G., R. Ren, W. Shang, Y. Weng, and J. Liu. Molecular dynamics simulation of polymer nanocomposites with supramolecular network constructed via functionalized polymer end-grafted nanoparticles. Polymers (Basel). 2023. https://doi.org/10.3390/polym15153259 . (PMID: 10.3390/polym151532593823199310708145)
Hou, S. H., F. F. Zhou, Y. H. Sun, and Q. Z. Li. Deconstructive and divergent synthesis of bioactive natural products. Molecules. 2023. https://doi.org/10.3390/molecules28176193 . (PMID: 10.3390/molecules281761933820271210779997)
Luu, K. T., E. Kraynov, B. Kuang, P. Vicini, and W. Z. Zhong. Modeling, simulation, and translation framework for the preclinical development of monoclonal antibodies. AAPS J. 2013. https://doi.org/10.1208/s12248-013-9464-8 . (PMID: 10.1208/s12248-013-9464-8234080943675753)
Lanman, B. A., J. R. Allen, J. G. Allen, A. K. Amegadzie, K. S. Ashton, S. K. Booker, J. J. Chen, N. Chen, M. J. Frohn, G. Goodman, D. J. Kopecky, L. Liu, P. Lopez, J. D. Low, V. Ma, A. E. Minatti, T. T. Nguyen, N. Nishimura, A. J. Pickrell, A. B. Reed, Y. Shin, A. C. Siegmund, N. A. Tamayo, C. M. Tegley, M. C. Walton, H. L. Wang, R. P. Wurz, M. Xue, K. C. Yang, P. Achanta, M. D. Bartberger, J. Canon, L. S. Hollis, J. D. McCarter, C. Mohr, K. Rex, A. Y. Saiki, T. San Miguel, L. P. Volak, K. H. Wang, D. A. Whittington, S. G. Zech, J. R. Lipford, and V. J. Cee. Discovery of a covalent inhibitor of KRASG12C (AMG 510) for the treatment of solid tumors. J. Med. Chem. 63:52–65, 2020. https://doi.org/10.1021/ACS.JMEDCHEM.9B01180/SUPPL_FILE/JM9B01180_SI_002.CSV . (PMID: 10.1021/ACS.JMEDCHEM.9B01180/SUPPL_FILE/JM9B01180_SI_002.CSV31820981)
Mateo, J., C. J. Lord, V. Serra, A. Tutt, J. Balmaña, M. Castroviejo-Bermejo, C. Cruz, A. Oaknin, S. B. Kaye, and J. S. De Bono. A decade of clinical development of PARP inhibitors in perspective. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 30:1437–1447, 2019. https://doi.org/10.1093/ANNONC/MDZ192 . (PMID: 10.1093/ANNONC/MDZ192)
Robichaux, J. P., Y. Y. Elamin, Z. Tan, B. W. Carter, S. Zhang, S. Liu, S. Li, T. Chen, A. Poteete, A. Estrada-Bernal, A. T. Le, A. Truini, M. B. Nilsson, H. Sun, E. Roarty, S. B. Goldberg, J. R. Brahmer, M. Altan, C. Lu, V. Papadimitrakopoulou, K. Politi, R. C. Doebele, K. K. Wong, and J. V. Heymach. Mechanisms and clinical activity of an EGFR and HER2 exon 20-selective kinase inhibitor in non-small cell lung cancer. Nat. Med. 24:638–646, 2018. https://doi.org/10.1038/s41591-018-0007-9 . (PMID: 10.1038/s41591-018-0007-9296864245964608)
Huang, M., A. Shen, J. Ding, and M. Geng. Molecularly targeted cancer therapy: some lessons from the past decade. Trends Pharmacol. Sci. 2014. https://doi.org/10.1016/j.tips.2013.11.004 . (PMID: 10.1016/j.tips.2013.11.00424361003)
Zargar, S. M., D. K. Hafshejani, A. Eskandarinia, M. Rafienia, and A. Z. Kharazi. A review of controlled drug delivery systems based on cells and cell membranes. J. Med. Signals Sens. 9:181, 2019. https://doi.org/10.4103/JMSS.JMSS_53_18 . (PMID: 10.4103/JMSS.JMSS_53_18315440586743242)
Kotschy, A., Z. Szlavik, J. Murray, J. Davidson, A. L. Maragno, G. Le Toumelin-Braizat, M. Chanrion, G. L. Kelly, J. N. Gong, D. M. Moujalled, A. Bruno, M. Csekei, A. Paczal, Z. B. Szabo, S. Sipos, G. Radics, A. Proszenyak, B. Balint, L. Ondi, G. Blasko, A. Robertson, A. Surgenor, P. Dokurno, I. Chen, N. Matassova, J. Smith, C. Pedder, C. Graham, A. Studeny, G. Lysiak-Auvity, A. M. Girard, F. Gravé, D. Segal, C. D. Riffkin, G. Pomilio, L. C. A. Galbraith, B. J. Aubrey, M. S. Brennan, M. J. Herold, C. Chang, G. Guasconi, N. Cauquil, F. Melchiore, N. Guigal-Stephan, B. Lockhart, F. Colland, J. A. Hickman, A. W. Roberts, D. C. S. Huang, A. H. Wei, A. Strasser, G. Lessene, and O. Geneste. The MCL1 inhibitor S63845 is tolerable and effective in diverse cancer models. Nature. 538:477–482, 2016. https://doi.org/10.1038/nature19830 . (PMID: 10.1038/nature1983027760111)
Murga, M., and O. Fernandez-Capetillo. Emerging concepts in drug discovery for cancer therapy. Mol. Oncol. 16:3757–3760, 2022. https://doi.org/10.1002/1878-0261.13325 . (PMID: 10.1002/1878-0261.13325363217229627784)
Bryant, H. E., N. Schultz, H. D. Thomas, K. M. Parker, D. Flower, E. Lopez, S. Kyle, M. Meuth, N. J. Curtin, and T. Helleday. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature. 434:913–917, 2005. https://doi.org/10.1038/nature03443 . (PMID: 10.1038/nature0344315829966)
Farmer, H., H. McCabe, C. J. Lord, A. H. J. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. B. Martin, S. P. Jackson, G. C. M. Smith, and A. Ashworth. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 434:917–921, 2005. https://doi.org/10.1038/nature03445 . (PMID: 10.1038/nature0344515829967)
Niggenaber, J., J. Hardick, J. Lategahn, and D. Rauh. Structure defines function: clinically relevant mutations in ErbB kinases. J. Med. Chem. 63:40–51, 2020. https://doi.org/10.1021/acs.jmedchem.9b00964 . (PMID: 10.1021/acs.jmedchem.9b0096431414802)
Melanoma Targeted Therapy | Targeted Drugs for Melanoma | American Cancer Society [WWW Document], n.d.
An, H. W., M. Mamuti, X. Wang, H. Yao, M. . Di. Wang, L. Zhao, and L. L. Li. Rationally designed modular drug delivery platform based on intracellular peptide self-assembly. Exploration. 2021. https://doi.org/10.1002/EXP.20210153 . (PMID: 10.1002/EXP.202101533732321710190849)
Ezike, T. C., U. S. Okpala, U. L. Onoja, C. P. Nwike, E. C. Ezeako, O. J. Okpara, C. C. Okoroafor, S. C. Eze, O. L. Kalu, E. C. Odoh, U. G. Nwadike, J. O. Ogbodo, B. U. Umeh, E. C. Ossai, and B. C. Nwanguma. Advances in drug delivery systems, challenges and future directions. Heliyon. 2023. https://doi.org/10.1016/J.HELIYON.2023.E17488 . (PMID: 10.1016/J.HELIYON.2023.E174883741668010320272)
Pasut, G. Grand challenges in nano-based drug delivery. Front. Med. Technol. 1:501667, 2019. https://doi.org/10.3389/FMEDT.2019.00001/BIBTEX . (PMID: 10.3389/FMEDT.2019.00001/BIBTEX)
Sharma, S., R. Parveen, and B. P. Chatterji. Toxicology of nanoparticles in drug delivery. Curr. Pathobiol. Rep. 9:133–144, 2021. https://doi.org/10.1007/S40139-021-00227-Z/FIGURES/2 . (PMID: 10.1007/S40139-021-00227-Z/FIGURES/2348409188611175)
Youn, Y. S., and Y. H. Bae. Perspectives on the past, present, and future of cancer nanomedicine. Adv. Drug Deliv. Rev. 130:3–11, 2018. https://doi.org/10.1016/J.ADDR.2018.05.008 . (PMID: 10.1016/J.ADDR.2018.05.00829778902)
Mahdavi, M., A. Fattahi, E. Tajkhorshid, and S. Nouranian. Molecular insights into the loading and dynamics of doxorubicin on PEGylated graphene oxide nanocarriers. ACS Appl. Bio Mater. 3:1354–1363, 2020. https://doi.org/10.1021/acsabm.9b00956 . (PMID: 10.1021/acsabm.9b00956333134827731932)
Albano, J. M. R., E. de Paula, and M. Pickholz. Molecular dynamics simulations to study drug delivery systems. Mol Dyn. 2018. https://doi.org/10.5772/intechopen.75748 . (PMID: 10.5772/intechopen.75748)
Pillay, S., V. Pillay, Y. E. Choonara, D. Naidoo, R. A. Khan, L. C. du Toit, V. M. K. Ndesendo, G. Modi, M. P. Danckwerts, and S. E. Iyuke. Design, biometric simulation and optimization of a nano-enabled scaffold device for enhanced delivery of dopamine to the brain. Int. J. Pharm. 382:277–290, 2009. https://doi.org/10.1016/j.ijpharm.2009.08.021 . (PMID: 10.1016/j.ijpharm.2009.08.02119703530)
Arabian, T., S. Amjad-Iranagh, and R. Halladj. Molecular dynamics simulation study of doxorubicin adsorption on functionalized carbon nanotubes with folic acid and tryptophan. Sci. Rep. 11:1–11, 2021. https://doi.org/10.1038/s41598-021-03619-8 . (PMID: 10.1038/s41598-021-03619-8)
Liu, Q., Z. Xue, and D. Xu. Molecular dynamics characterization of Sr-doped biomimetic hydroxyapatite nanoparticles. J. Phys. Chem. C. 124:19704–19715, 2020. https://doi.org/10.1021/acs.jpcc.0c06391 . (PMID: 10.1021/acs.jpcc.0c06391)
Kuhlman, B., and P. Bradley. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 2019. https://doi.org/10.1038/s41580-019-0163-x . (PMID: 10.1038/s41580-019-0163-x314171967032036)
Salo-Ahen, O. M. H., I. Alanko, R. Bhadane, A. M. Alexandre, R. V. Honorato, S. Hossain, A. H. Juffer, A. Kabedev, M. Lahtela-Kakkonen, A. S. Larsen, E. Lescrinier, P. Marimuthu, M. U. Mirza, G. Mustafa, A. Nunes-Alves, T. Pantsar, A. Saadabadi, K. Singaravelu, and M. Vanmeert. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 2021. https://doi.org/10.3390/pr9010071 . (PMID: 10.3390/pr9010071)
Hasanzade, Z., and H. Raissi. Solvent/co-solvent effects on the electronic properties and adsorption mechanism of anticancer drug Thioguanine on Graphene oxide surface as a nanocarrier: density functional theory investigation and a molecular dynamics. Appl. Surf. Sci. 2017. https://doi.org/10.1016/j.apsusc.2017.05.245 . (PMID: 10.1016/j.apsusc.2017.05.245)
Mehta, M., T. A. Bui, X. Yang, Y. Aksoy, E. M. Goldys, and W. Deng. Lipid-based nanoparticles for drug/gene delivery: an overview of the production techniques and difficulties encountered in their industrial development. ACS Mater. Au. 2023. https://doi.org/10.1021/acsmaterialsau.3c00032 . (PMID: 10.1021/acsmaterialsau.3c000323808966610636777)
Liao, S., L. Wei, A. E. Bouchez, and F. Stellacci. Influence of structural dynamics on cell uptake investigated with single-chain polymeric nanoparticles. Chem. 2023. https://doi.org/10.1016/j.chempr.2023.03.012 . (PMID: 10.1016/j.chempr.2023.03.012)
Abedi-Gaballu, F., G. Dehghan, M. Ghaffari, R. Yekta, S. Abbaspour-Ravasjani, B. Baradaran, J. Ezzati Nazhad Dolatabadi, and M. R. Hamblin. PAMAM dendrimers as efficient drug and gene delivery nanosystems for cancer therapy. Appl. Mater. Today. 2018. https://doi.org/10.1016/j.apmt.2018.05.002 . (PMID: 10.1016/j.apmt.2018.05.002305110146269116)
Daniyal, M., B. Liu, and W. Wang. Comprehensive review on graphene oxide for use in drug delivery system. Curr. Med. Chem. 2020. https://doi.org/10.2174/13816128256661902011296290 . (PMID: 10.2174/1381612825666190201129629030767730)
He, S., L. Wu, X. Li, H. Sun, T. Xiong, J. Liu, C. Huang, H. Xu, H. Sun, W. Chen, R. Gref, and J. Zhang. Metal-organic frameworks for advanced drug delivery. Acta Pharm. Sin. B. 2021. https://doi.org/10.1016/j.apsb.2021.03.019 . (PMID: 10.1016/j.apsb.2021.03.019358475189136566)
Chen, D., X. Liu, X. Lu, and J. Tian. Nanoparticle drug delivery systems for synergistic delivery of tumor therapy. Front. Pharmacol. 2023. https://doi.org/10.3389/fphar.2023.1111991 . (PMID: 10.3389/fphar.2023.11119913863833410777313)
Zhang, M., J. Zhu, Y. Zheng, R. Guo, S. Wang, S. Mignani, A. M. Caminade, J. P. Majoral, and X. Shi. Doxorubicin-conjugated PAMAM dendrimers for pH-responsive drug release and folic acid-targeted cancer therapy. Pharmaceutics. 2018. https://doi.org/10.3390/pharmaceutics10030162 . (PMID: 10.3390/pharmaceutics10030162305836016359657)
Liu, J., Y. Zhang, C. Liu, Y. Jiang, Z. Wang, and X. Li. Paclitaxel prodrug-encapsulated polypeptide micelles with redox/pH dual responsiveness for cancer chemotherapy. Int. J. Pharm. 2023. https://doi.org/10.1016/j.ijpharm.2023.123398 . (PMID: 10.1016/j.ijpharm.2023.1233983816099011124712)
Pourmadadi, M., M. M. Eshaghi, E. Rahmani, N. Ajalli, S. Bakhshi, H. Mirkhaef, M. V. Lasemi, A. Rahdar, R. Behzadmehr, and A. M. Díez-Pascual. Cisplatin-loaded nanoformulations for cancer therapy: a comprehensive review. J. Drug Deliv. Sci. Technol. 2022. https://doi.org/10.1016/j.jddst.2022.103928 . (PMID: 10.1016/j.jddst.2022.103928)
Ganthala, P. D., S. Alavala, N. Chella, S. B. Andugulapati, N. B. Bathini, and R. Sistla. Co-encapsulated nanoparticles of Erlotinib and Quercetin for targeting lung cancer through nuclear EGFR and PI3K/AKT inhibition. Colloids Surfaces B Biointerfaces. 2022. https://doi.org/10.1016/j.colsurfb.2021.112305 . (PMID: 10.1016/j.colsurfb.2021.11230534998178)
Khademi, R., Z. Mohammadi, R. Khademi, A. Saghazadeh, and N. Rezaei. Nanotechnology-based diagnostics and therapeutics in acute lymphoblastic leukemia: a systematic review of preclinical studies. Nanoscale Adv. 2023. https://doi.org/10.1039/d2na00483f . (PMID: 10.1039/d2na00483f367565029890594)
Wang, L., M. Chen, X. Ran, H. Tang, and D. Cao. Sorafenib-based drug delivery systems: applications and perspectives. Polymers (Basel). 2023. https://doi.org/10.3390/polym15122638 . (PMID: 10.3390/polym151226383823203010781181)
Zhu, L., Q. Mu, J. Yu, J. I. Griffin, X. Xu, and R. J. Y. Ho. ICAM-1 targeted drug combination nanoparticles enhanced gemcitabine-paclitaxel exposure and breast cancer suppression in mouse models. Pharmaceutics. 2022. https://doi.org/10.3390/pharmaceutics14010089 . (PMID: 10.3390/pharmaceutics14010089366787699865350)
Parvez, A., F. Choudhary, P. Mudgal, R. Khan, K. A. Qureshi, H. Farooqi, and A. Aspatwar. PD-1 and PD-L1: architects of immune symphony and immunotherapy breakthroughs in cancer treatment. Front. Immunol. 2023. https://doi.org/10.3389/fimmu.2023.1296341 . (PMID: 10.3389/fimmu.2023.12963413810641510722272)
Abdi, F., E. Arkan, K. Mansouri, Z. Shekarbeygi, and E. Barzegari. Interactions of Bevacizumab with chitosan biopolymer nanoparticles: Molecular modeling and spectroscopic study. J. Mol. Liq. 2021. https://doi.org/10.1016/j.molliq.2021.116655 . (PMID: 10.1016/j.molliq.2021.116655)
Suzuki, Y., and H. Ishihara. Difference in the lipid nanoparticle technology employed in three approved siRNA (Patisiran) and mRNA (COVID-19 vaccine) drugs. Drug Metab. Pharmacokinet. 2021. https://doi.org/10.1016/j.dmpk.2021.100424 . (PMID: 10.1016/j.dmpk.2021.100424347572878502116)
Yonezawa, S., H. Koide, and T. Asai. Recent advances in siRNA delivery mediated by lipid-based nanoparticles. Adv. Drug Deliv. Rev. 2020. https://doi.org/10.1016/j.addr.2020.07.022 . (PMID: 10.1016/j.addr.2020.07.022327685647406478)
Yeo, S., M. J. Kim, Y. K. Shim, I. Yoon, and W. K. Lee. Solid lipid nanoparticles of curcumin designed for enhanced bioavailability and anticancer efficiency. ACS Omega. 2022. https://doi.org/10.1021/acsomega.2c04407 . (PMID: 10.1021/acsomega.2c04407362493829558702)
Hosseini Berenji, R., A. Pezeshki, B. Ghanbarzadeh, M. Mohammadi, M. Tabibi Azar, H. Hamishehkar, F. Ahmadzadeh Nobari Azar, and M. Ghorbani. Resveratrol entrapped food grade lipid nanocarriers as a potential antioxidant in a mayonnaise. Food Biosci. 2021. https://doi.org/10.1016/j.fbio.2021.101041 . (PMID: 10.1016/j.fbio.2021.101041)
Jain, K. K. An overview of drug delivery systems. Methods Mol Biol. 2020. https://doi.org/10.1007/978-1-4939-9798-5_1 . (PMID: 10.1007/978-1-4939-9798-5_131893451)
Lomzov, A. A., Y. N. Vorobjev, and D. V. Pyshnyi. Evaluation of the Gibbs free energy changes and melting temperatures of DNA/DNA duplexes using hybridization enthalpy calculated by molecular dynamics simulation. J. Phys. Chem. B. 2015. https://doi.org/10.1021/acs.jpcb.5b09645 . (PMID: 10.1021/acs.jpcb.5b09645265691474824051)
Zhu, S., N. Lempesis, P. J. In ‘T Veld, and G. C. Rutledge. Molecular simulation of thermoplastic polyurethanes under large tensile deformation. Macromolecules. 2018. https://doi.org/10.1021/acs.macromol.7b02367 . (PMID: 10.1021/acs.macromol.7b02367315379476752221)
Schichtel, J. J., and A. Chattopadhyay. Modeling thermoset polymers using an improved molecular dynamics crosslinking methodology. Comput. Mater. Sci. 2020. https://doi.org/10.1016/j.commatsci.2019.109469 . (PMID: 10.1016/j.commatsci.2019.109469)
Chen, Q., Z. Zhang, Y. Huang, H. Zhao, Z. Chen, K. Gao, T. Yue, L. Zhang, and J. Liu. Structure−mechanics relation of natural rubber: insights from molecular dynamics simulations. ACS Appl. Polym. Mater. 2022. https://doi.org/10.1021/acsapm.2c00147 . (PMID: 10.1021/acsapm.2c00147)
Shen, K. H., M. Fan, and L. M. Hall. Molecular dynamics simulations of ion-containing polymers using generic coarse-grained models. Macromolecules. 2021. https://doi.org/10.1021/acs.macromol.0c02557 . (PMID: 10.1021/acs.macromol.0c02557)
Siani, P., E. Donadoni, L. Ferraro, F. Re, and C. Di Valentin. Molecular dynamics simulations of doxorubicin in sphingomyelin-based lipid membranes. Biochim. Biophys. Acta—Biomembr. 2022. https://doi.org/10.1016/j.bbamem.2021.183763 . (PMID: 10.1016/j.bbamem.2021.18376334506799)
Kharazian, B., A. A. Ahmad, and A. Mabudi. A molecular dynamics study on the binding of gemcitabine to human serum albumin. J. Mol. Liq. 337:116496, 2021. https://doi.org/10.1016/J.MOLLIQ.2021.116496 . (PMID: 10.1016/J.MOLLIQ.2021.116496)
Lim, J., S. T. Lo, S. Hill, G. M. Pavan, X. Sun, and E. E. Simanek. Antitumor activity and molecular dynamics simulations of paclitaxel-laden triazine dendrimers. Mol. Pharm. 9:404, 2012. https://doi.org/10.1021/MP2005017 . (PMID: 10.1021/MP2005017222603287768605)
Dong-Dong Li, Ting-Ting Wu, Pan Yu, Zhen-Zhong Wang, Wei Xiao, Yan Jiang, Lin-Guo Zhao. Molecular Dynamics Analysis of Binding Sites of Epidermal Growth Factor Receptor Kinase Inhibitors. ACS Omega. 5:26, 2020. https://doi.org/10.1021/acsomega.0c02183 .
Owen, A. E., C. M. Chima, I. Ahmad, W. Emori, E. C. Agwamba, C. R. Cheng, I. Benjamin, H. Patel, E. F. Ahuekwe, M. A. Ojong, C. B. Ubah, A. L. E. Manicum, and H. Louis. Antibacterial potential of trihydroxycyclohexa-2,4-diene-1-carboxylic acid: insight from DFT, molecular docking, and molecular dynamic simulation. Polycycl. Aromat. Compd. 2024. https://doi.org/10.1080/10406638.2023.2214280 . (PMID: 10.1080/10406638.2023.2214280)
Jiao, Y., C. Shi, and Y. Sun. Unraveling the Role of scutellaria baicalensis for the treatment of breast cancer using network pharmacology, molecular docking, and molecular dynamics simulation. Int. J. Mol. Sci. 2023. https://doi.org/10.3390/ijms24043594 . (PMID: 10.3390/ijms240435943820343310779386)
Isa, M. A., and A. P. Kappo. Exploring phytoconstituents through molecular dynamics simulation: uncovering potential inhibitors for multiple targeted pathways in breast cancer. J. Proteins Proteomics. 16:125–140, 2025. https://doi.org/10.1007/s42485-025-00176-w . (PMID: 10.1007/s42485-025-00176-w)
Rashidieh, B., M. Valizadeh, V. Assadollahi, and M. M. Ranjbar. Molecular dynamics simulation on the low sensitivity of mutants of NEDD-8 activating enzyme for MLN4924 inhibitor as a cancer drug. Am. J. Cancer Res. 5:3400–3406, 2015. (PMID: 268073204697686)
Johnson-Arbor, K., and R. Dubey. Doxorubicin. xPharm Compr. Pharmacol. Ref. 2023. https://doi.org/10.1016/B978-008055232-3.61650-2 . (PMID: 10.1016/B978-008055232-3.61650-2)
Kotzabasaki, M., I. Galdadas, E. Tylianakis, E. Klontzas, Z. Cournia, and G. E. Froudakis. Multiscale simulations reveal IRMOF-74-III as a potent drug carrier for gemcitabine delivery. J. Mater. Chem. B. 5:3277–3282, 2017. https://doi.org/10.1039/c7tb00220c . (PMID: 10.1039/c7tb00220c32264393)
Wang, X. Y., L. Zhang, X. H. Wei, and Q. Wang. Molecular dynamics of paclitaxel encapsulated by salicylic acid-grafted chitosan oligosaccharide aggregates. Biomaterials. 34:1843–1851, 2013. https://doi.org/10.1016/J.BIOMATERIALS.2012.11.024 . (PMID: 10.1016/J.BIOMATERIALS.2012.11.02423219327)
Azizi, A., and S. Ebrahimi. Molecular dynamics study of PTX adsorption onto n-doped graphene in vacuum and aqueous environments. Nano. 9:1, 2015. https://doi.org/10.1142/S179329201450088X . (PMID: 10.1142/S179329201450088X)
del De Lama-Odría, M. C., L. J. del Valle, and J. Puiggalí. Hydroxyapatite biobased materials for treatment and diagnosis of cancer. Int. J. Mol. Sci. 23:11352, 2022. https://doi.org/10.3390/ijms231911352 . (PMID: 10.3390/ijms231911352362326529569977)
Zhu, C., X. Zhou, Z. Liu, H. Chen, H. Wu, X. Yang, X. Zhu, J. Ma, and H. Dong. The morphology of hydroxyapatite nanoparticles regulates cargo recognition in clathrin-mediated endocytosis. Front. Mol. Biosci. 2021. https://doi.org/10.3389/FMOLB.2021.627015/FULL . (PMID: 10.3389/FMOLB.2021.627015/FULL352656668740137)
Hu, J., Zhou, L., Jiang, J., 2023. Efficient machine learning force field for large-scale molecular simulations of organic systems. https://doi.org/10.31635/ccschem.024.202404785.
Mollahosseini, A., and A. Abdelrasoul. Molecular dynamics simulation for membrane separation and porous materials: a current state of art review. J. Mol. Graph. Model. 2021. https://doi.org/10.1016/J.JMGM.2021.107947 . (PMID: 10.1016/J.JMGM.2021.10794734126546)
Kuhlman, B., and P. Bradley. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20:681–697, 2019. https://doi.org/10.1038/s41580-019-0163-x . (PMID: 10.1038/s41580-019-0163-x314171967032036)
Zare, H., S. Ahmadi, A. Ghasemi, M. Ghanbari, N. Rabiee, M. Bagherzadeh, M. Karimi, T. J. Webster, M. R. Hamblin, and E. Mostafavi. Carbon nanotubes: smart drug/gene delivery carriers. Int. J. Nanomedicine. 2021. https://doi.org/10.2147/IJN.S299448 . (PMID: 10.2147/IJN.S299448336881857936533)
Perilla, J. R., B. C. Goh, C. K. Cassidy, B. Liu, R. C. Bernardi, T. Rudack, H. Yu, Z. Wu, and K. Schulten. Molecular dynamics simulations of large macromolecular complexes. Curr. Opin. Struct. Biol. 31:64–74, 2015. https://doi.org/10.1016/J.SBI.2015.03.007 . (PMID: 10.1016/J.SBI.2015.03.007258457704476923)
Noé, F., A. Tkatchenko, K. R. Müller, and C. Clementi. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–390, 2020. https://doi.org/10.1146/annurev-physchem-042018-052331 . (PMID: 10.1146/annurev-physchem-042018-05233132092281)
Lei, H., X. Li, J. Wang, Y. Song, G. Tian, M. Huang, and D. Wu. DFT and molecular dynamic simulation for the dielectric property analysis of polyimides. Chem. Phys. Lett. 2022. https://doi.org/10.1002/jcc.21787 . (PMID: 10.1016/j.cplett.2021.139131)
Michaud-Agrawal, N., E. J. Denning, T. B. Woolf, and O. Beckstein. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32(10):2319–2327, 2011. https://doi.org/10.1002/jcc.v32.10.1002/jcc.21787 .
Pronk, S., S. Páll, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J. C. Smith, P. M. Kasson, D. van der Spoel, B. Hess, and E. Lindahl. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 29(7) 845–854, 2013. https://doi.org/10.1093/bioinformatics/btt055 .
Zhang, H., W. Liao, G. Chen, and H. Ma. Development and characterization of coal-based thermoplastic composite material for sustainable construction. Sustainability. 15(16):12446, 2023. https://doi.org/10.3390/su151612446 .
Zhang, Z. W., and W. C. Lu. AmberMDrun: a scripting tool for running Amber MD in an easy way. Biomolecules. 13(4):635, 2023. https://doi.org/10.3390/biom13040635 .
Thompson, A. P., H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton (2022) LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic meso and continuum scales. Comput. Phys. Commun. 271108171. https://doi.org/10.1016/j.cpc.2021.108171 . (PMID: 10.1016/j.cpc.2021.108171)
Phillips, J. C., D. J. Hardy, J. D. C. Maia, J. E. Stone, J. V. Ribeiro, R. C. Bernardi, R. Buch, G. Fiorin, J. Hénin, W. Jiang, R. McGreevy, M. C. R. Melo, B. K. Radak, R. D. Skeel, A. Singharoy, Y. Wang, B. Roux, A. Aksimentiev, Z. Luthey-Schulten, L. V. Kalé, K. Schulten, C. Chipot, E. Tajkhorshid. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 153(4), 2020. https://doi.org/10.1063/5.0014475 .
Jeevanandam, J., K. X. Tan, M. K. Danquah, H. Guo, and A. Turgeson. Advancing aptamers as molecular probes for cancer theranostic applications—the role of molecular dynamics simulation. Biotechnol. J. 15:1900368, 2020. https://doi.org/10.1002/BIOT.201900368 . (PMID: 10.1002/BIOT.201900368)
Li, X., F. Miao, R. Xin, Z. Tai, H. Pan, H. Huang, J. Yu, Z. Chen, and Q. Zhu. Combining network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification to examine the efficacy and immunoregulation mechanism of FHB granules on vitiligo. Front. Immunol. 2023. https://doi.org/10.3389/fimmu.2023.1194823 . (PMID: 10.3389/fimmu.2023.11948233839039710777728)
Lopes, D., S. Jakobtorweihen, C. Nunes, B. Sarmento, and S. Reis. Shedding light on the puzzle of drug-membrane interactions: experimental techniques and molecular dynamics simulations. Prog. Lipid Res. 2017. https://doi.org/10.1016/j.plipres.2016.12.001 . (PMID: 10.1016/j.plipres.2016.12.00127939295)
Xu, X., A. Liu, S. Liu, Y. Ma, X. Zhang, M. Zhang, J. Zhao, S. Sun, and X. Sun. Application of molecular dynamics simulation in self-assembled cancer nanomedicine. Biomater. Res. 27:1–32, 2023. https://doi.org/10.1186/s40824-023-00386-7 . (PMID: 10.1186/s40824-023-00386-7)
Barbhuiya, S., and B. B. Das. Molecular dynamics simulation in concrete research: a systematic review of techniques, models and future directions. J. Build. Eng. 2023. https://doi.org/10.1016/j.jobe.2023.107267 . (PMID: 10.1016/j.jobe.2023.107267)
Borhani, D. W., and D. E. Shaw. The future of molecular dynamics simulations in drug discovery. J. Comput. Aided. Mol. Des. 26:15–26, 2012. https://doi.org/10.1007/s10822-011-9517-y . (PMID: 10.1007/s10822-011-9517-y22183577)
Durrant, J. D., and J. A. McCammon. Molecular dynamics simulations and drug discovery. BMC Biol. 2011. https://doi.org/10.1186/1741-7007-9-71 . (PMID: 10.1186/1741-7007-9-71220354603203851)
Hollingsworth, S. A., and R. O. Dror. Molecular dynamics simulation for all. Neuron. 99:1129, 2018. https://doi.org/10.1016/J.NEURON.2018.08.011 . (PMID: 10.1016/J.NEURON.2018.08.011302362836209097)
Bunker, A., and T. Róg. Mechanistic understanding from molecular dynamics simulation in pharmaceutical research 1: drug delivery. Front. Mol. Biosci. 2020. https://doi.org/10.3389/fmolb.2020.604770 . (PMID: 10.3389/fmolb.2020.604770333306337732618)
Duran, T., A. Costa, A. Gupta, X. Xu, H. Zhang, D. Burgess, and B. Chaudhuri. Coarse-grained molecular dynamics simulations of paclitaxel-loaded polymeric micelles. Mol. Pharm. 19:1117–1134, 2022. https://doi.org/10.1021/acs.molpharmaceut.1c00800 . (PMID: 10.1021/acs.molpharmaceut.1c0080035243863)
Ashwini, T., R. Narayan, P. A. Shenoy, and U. Y. Nayak. Computational modeling for the design and development of nano based drug delivery systems. J. Mol. Liq. 2022. https://doi.org/10.1016/j.molliq.2022.120596 . (PMID: 10.1016/j.molliq.2022.120596)
Farhadian, N., M. S. Kazemi, F. Moosavi Baigi, and M. Khalaj. Molecular dynamics simulation of drug delivery across the cell membrane by applying gold nanoparticle carrier: flutamide as hydrophobic and glutathione as hydrophilic drugs as the case studies. J. Mol. Graph. Model. 2022. https://doi.org/10.1016/j.jmgm.2022.108271 . (PMID: 10.1016/j.jmgm.2022.10827135863117)
Ivánczi, M., B. Balogh, L. Kis, and I. Mándity. Molecular dynamics simulations of drug-conjugated cell-penetrating peptides. Pharmaceuticals. 2023. https://doi.org/10.3390/ph16091251 . (PMID: 10.3390/ph160912513776505910535489)
Martinotti, C., L. Ruiz-Perez, E. Deplazes, and R. L. Mancera. Molecular dynamics simulation of small molecules interacting with biological membranes. ChemPhysChem. 2020. https://doi.org/10.1002/cphc.202000219 . (PMID: 10.1002/cphc.20200021932452115)
Badar, M. S., S. Shamsi, J. Ahmed, and M. A. Alam. Molecular Dynamics Simulations: Concept, Methods, and Applications. Springer Nature, pp. 131–151, 2022.
Hénin, J., T. Lelièvre, M. R. Shirts, O. Valsson, and L. Delemotte. Enhanced sampling methods for molecular dynamics simulations [Article v1.0]. Living J. Comput. Mol. Sci. 2022. https://doi.org/10.33011/livecoms.4.1.1583 . (PMID: 10.33011/livecoms.4.1.1583)
Singh, S., and V. K. Singh. Molecular dynamics simulation: methods and application. In: Frontiers in Protein Structure, Function, and Dynamics, Springer, 2020, pp. 213–238. (PMID: 10.1007/978-981-15-5530-5_9)
Lee, H. S., Y. Qi, and W. Im. Effects of N-glycosylation on protein conformation and dynamics: Protein Data Bank analysis and molecular dynamics simulation study. Sci. Rep. 2015. https://doi.org/10.1038/srep08926 . (PMID: 10.1038/srep08926266919294686891)
Shahapure, N., D. Shinde, and A. Kelkar. Atomistic modeling and molecular dynamic simulation of polymer nanocomposites for thermal and mechanical property characterization: a review. AIMS Mater. Sci. 10:249–287, 2023. https://doi.org/10.3934/matersci.2023014 . (PMID: 10.3934/matersci.2023014)
Swai, R. E. A review of molecular dynamics simulations in the designing of effective shale inhibitors: application for drilling with water-based drilling fluids. J. Pet. Explor. Prod. Technol. 2020. https://doi.org/10.1007/s13202-020-01003-2 . (PMID: 10.1007/s13202-020-01003-2)
Pawnikar, S., A. Bhattarai, J. Wang, and Y. Miao. Binding analysis using accelerated molecular dynamics simulations and future perspectives. Adv. Appl. Bioinforma. Chem. 15:1–19, 2022. https://doi.org/10.2147/AABC.S247950 . (PMID: 10.2147/AABC.S247950)
Lindorff-Larsen, K., P. Maragakis, S. Piana, M. P. Eastwood, R. O. Dror, and D. E. Shaw. Systematic validation of protein force fields against experimental data. PLoS One. 7:e32131, 2012. https://doi.org/10.1371/JOURNAL.PONE.0032131 . (PMID: 10.1371/JOURNAL.PONE.0032131223841573285199)
Senn, H. M., and W. Thiel. QM/MM methods for biomolecular systems. Angew. Chemie—Int. Ed. 2009. https://doi.org/10.1002/anie.200802019 . (PMID: 10.1002/anie.200802019)
Sapay, N., and D. P. Tieleman. Chapter 4 Molecular dynamics simulation of lipid-protein interactions. Curr. Top. Membr. 2008. https://doi.org/10.1016/S1063-5823(08)00004-5 . (PMID: 10.1016/S1063-5823(08)00004-5)
Wang, Y., C. B. Harrison, K. Schulten, and J. A. McCammon. Implementation of accelerated molecular dynamics in NAMD. Comput. Sci. Discov. 2011. https://doi.org/10.1088/1749-4699/4/1/015002 . (PMID: 10.1088/1749-4699/4/1/015002216860633115733)
Al-Akhras, M. A., B. Odat, V. Narayanaswamy, M. S. Mousa, B. Issa, M. Obeidat, I. A. Al-Omari, and I. M. Obaidat. Molecular simulations of 6-gingerol loading on graphene and graphene oxide for drug delivery applications. Biointerface Res. Appl. Chem. 2023. https://doi.org/10.33263/BRIAC133.258 . (PMID: 10.33263/BRIAC133.258)
Arslan, M. B. Comparative analysis of mitoxantrone and doxorubicin interactions with single-walled carbon nanotubes using molecular dynamics simulations. El-Cezeri J. Sci. Eng. 10:656–666, 2023. https://doi.org/10.31202/ecjse.1345238 . (PMID: 10.31202/ecjse.1345238)
Gocheva, G., and A. Ivanova. A look at receptor-ligand pairs for active-targeting drug delivery from crystallographic and molecular dynamics perspectives. Mol. Pharm. 16:3293–3321, 2019. https://doi.org/10.1021/acs.molpharmaceut.9b00250 . (PMID: 10.1021/acs.molpharmaceut.9b0025031274322)
Liang, L., J. W. Shen, and Q. Wang. Molecular dynamics study on DNA nanotubes as drug delivery vehicle for anticancer drugs. Colloids Surfaces B Biointerfaces. 153:168–173, 2017. https://doi.org/10.1016/j.colsurfb.2017.02.021 . (PMID: 10.1016/j.colsurfb.2017.02.02128237820)
Lee, H. Molecular simulations of pegylated biomolecules, liposomes, and nanoparticles for drug delivery applications. Pharmaceutics. 2020. https://doi.org/10.3390/pharmaceutics12060533 . (PMID: 10.3390/pharmaceutics12060533333792957823613)
Saurabh, S., P. M. Sivakumar, V. Perumal, A. Khosravi, A. Sugumaran, and V. Prabhawathi. Molecular dynamics simulations in drug discovery and drug delivery. In: Engineering Materials, Springer, 2020, pp. 275–301.
Shariatinia, Z., 2021a. Molecular dynamics simulations on drug delivery systems, in: Modeling and Control of Drug Delivery Systems. pp. 153–182. https://doi.org/10.1016/B978-0-12-821185-4.00013-0.
Razmimanesh, F., S. Amjad-Iranagh, and H. Modarress. Molecular dynamics simulation study of chitosan and gemcitabine as a drug delivery system. J. Mol. Model. 2015. https://doi.org/10.1007/S00894-015-2705-2 . (PMID: 10.1007/S00894-015-2705-226044358)
Salahshoori, I., M. Golriz, M. A. L. Nobre, S. Mahdavi, R. Eshaghi Malekshah, A. Javdani-Mallak, M. Namayandeh Jorabchi, H. Ali Khonakdar, Q. Wang, A. H. Mohammadi, S. Masoomeh Sadat Mirnezami, and F. Kargaran. Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges. J. Mol. Liq. 2024. https://doi.org/10.1016/j.molliq.2023.123888 . (PMID: 10.1016/j.molliq.2023.123888)
Shariatinia, Z., and A. Mazloom-Jalali. Molecular dynamics simulations on chitosan/graphene nanocomposites as anticancer drug delivery using systems. Chinese J. Phys. 66:362–382, 2020. https://doi.org/10.1016/j.cjph.2020.04.012 . (PMID: 10.1016/j.cjph.2020.04.012)
Brooks, B. R., C. L. Brooks, A. D. Mackerell, L. Nilsson, R. J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York, and M. Karplus. CHARMM: the biomolecular simulation program. J. Comput. Chem. 30:1545–1614, 2009. https://doi.org/10.1002/jcc.21287 . (PMID: 10.1002/jcc.21287194448162810661)
Zeng, K., and K. Chen. Molecular dynamics simulation calculation method for elasticity and plasticity of metal nanostructures. Adv. Mater. Sci. Eng. 2022. https://doi.org/10.1155/2022/6264256 . (PMID: 10.1155/2022/6264256)
Frenkel, D., Smit, B., 2023. Understanding Molecular Simulation. https://doi.org/10.1016/c2009-0-63921-0.
Berendsen, H. J. C., and W. F. Gunsteren. Molecular dynamics simulations: techniques and approaches. In: Molecular Liquids, Springer, 1984, pp. 475–500. (PMID: 10.1007/978-94-009-6463-1_16)
Konczak, L., J. Narkiewicz-Michalek, G. Pastorin, and T. Panczyk. Effects of intermolecular interactions on the stability of carbon nanotube–gold nanoparticle conjugates in solution. Int. J. Nanomed. 11:5837–5849, 2016. https://doi.org/10.2147/IJN.S117858 . (PMID: 10.2147/IJN.S117858)
Latorraca, N. R., N. M. Fastman, A. J. Venkatakrishnan, W. B. Frommer, R. O. Dror, and L. Feng. Mechanism of substrate translocation in an alternating access transporter. Cell. 169:96-107.e12, 2017. https://doi.org/10.1016/j.cell.2017.03.010 . (PMID: 10.1016/j.cell.2017.03.010283403545557413)
Kappel, K., Y. Miao, and J. Andrew McCammon. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q. Rev. Biophys. 48:479–487, 2015. https://doi.org/10.1017/S0033583515000153 . (PMID: 10.1017/S0033583515000153265374085435230)
Shang, S., Q. Zhao, D. Zhang, R. Sun, and Y. Tang. Molecular dynamics simulation of the adsorption behavior of two different drugs on hydroxyapatite and Zn-doped hydroxyapatite. Mater. Sci. Eng. C. 105:110017, 2019. https://doi.org/10.1016/j.msec.2019.110017 . (PMID: 10.1016/j.msec.2019.110017)
Wang, Q., M. Wang, X. Lu, K. Wang, L. Fang, F. Ren, and G. Lu. Effects of atomic-level nano-structured hydroxyapatite on adsorption of bone morphogenetic protein-7 and its derived peptide by computer simulation. Sci. Rep. 7:1–14, 2017. https://doi.org/10.1038/s41598-017-15219-6 . (PMID: 10.1038/s41598-017-15219-6281270515428335)
Wang, Q., M. H. Wang, K. F. Wang, Y. Liu, H. P. Zhang, X. Lu, and X. D. Zhang. Computer simulation of biomolecule-biomaterial interactions at surfaces and interfaces. Biomed. Mater. 2015. https://doi.org/10.1088/1748-6041/10/3/032001 . (PMID: 10.1088/1748-6041/10/3/03200126694757)
Uddin, N. M., F. M. Capaldi, and B. Farouk. Molecular dynamics simulations of the interactions and dispersion of carbon nanotubes in polyethylene oxide/water systems. Polymer (Guildf). 52:288–296, 2011. https://doi.org/10.1016/j.polymer.2010.11.056 . (PMID: 10.1016/j.polymer.2010.11.056)
Rsheed, A. . Al., S. Aldawood, and O. M. Aldossary. The size and shape effects on the melting point of nanoparticles based on the lennard-jones potential function. Nanomaterials. 2021. https://doi.org/10.3390/nano11112916 . (PMID: 10.3390/nano11112916348356808621700)
Fijany, A., T. Çagin, A. Jaramillo-Botero, and W. Goddard. Novel algorithms for massively parallel, long-term, simulation of molecular dynamics systems. Adv. Eng. Softw. 1998. https://doi.org/10.1016/S0965-9978(98)00053-2 . (PMID: 10.1016/S0965-9978(98)00053-2)
Chen, K., and K. Zeng. Performance optimization model of molecular dynamics simulation based on machine learning and data mining algorithm. Mob. Inf. Syst. 2022. https://doi.org/10.1155/2022/4553446 . (PMID: 10.1155/2022/4553446)
Wu, J., W. Sang, D. Li, and L. Jin. Molecular dynamics simulation of temperature effects on sodium chloride solution adsorption in γ-FeOOH nanopores. Constr. Build. Mater. 449:138410, 2024. https://doi.org/10.1016/j.conbuildmat.2024.138410 . (PMID: 10.1016/j.conbuildmat.2024.138410)
You, D., H. Wang, W. Sun, L. Wang, H. Zhang, X. Chen, and G. Liu. Understanding the effect of temperature, concentration, and substrate material on CaCO3 scaling: molecular dynamics simulations and density functional theory. Comput. Mater. Sci. 2022. https://doi.org/10.1016/j.commatsci.2022.111352 . (PMID: 10.1016/j.commatsci.2022.111352)
Barazorda-Ccahuana, H. L., M. Nedyalkova, F. Mas, and S. Madurga. Unveiling the effect of low ph on the sars-cov-2 main protease by molecular dynamics simulations. Polymers (Basel). 2021. https://doi.org/10.3390/polym13213823 . (PMID: 10.3390/polym1321382334771379)
Lv, X., H. Liu, M. Ke, and H. Gong. Exploring the pH-dependent substrate transport mechanism of FocA using molecular dynamics simulation. Biophys. J. 2013. https://doi.org/10.1016/j.bpj.2013.11.006 . (PMID: 10.1016/j.bpj.2013.11.006243597433882453)
Mukherjee, S., M. Abdalla, M. Yadav, M. Madhavi, A. Bhrdwaj, R. Khandelwal, L. Prajapati, A. Panicker, A. Chaudhary, A. Albrakati, T. Hussain, A. Nayarisseri, and S. K. Singh. Structure-based virtual screening, molecular docking, and molecular dynamics simulation of VEGF inhibitors for the clinical treatment of ovarian cancer. J. Mol. Model. 2022. https://doi.org/10.1007/s00894-022-05081-3 . (PMID: 10.1007/s00894-022-05081-335325303)
Gopinath, P., and M. K. Kathiravan. Docking studies and molecular dynamics simulation of triazole benzene sulfonamide derivatives with human carbonic anhydrase IX inhibition activity. RSC Adv. 2021. https://doi.org/10.1039/d1ra07377j . (PMID: 10.1039/d1ra07377j354807619038044)
Shuel, S. L. Targeted cancer therapies. Can. Fam. Physician. 68:515–518, 2022. https://doi.org/10.46747/cfp.6807515 . (PMID: 10.46747/cfp.6807515358310919842142)
Hou, G., R. Ren, W. Shang, Y. Weng, and J. Liu. Molecular dynamics simulation of polymer nanocomposites with supramolecular network constructed via functionalized polymer end-grafted nanoparticles. Polymers (Basel). 2023. https://doi.org/10.3390/polym15153259 . (PMID: 10.3390/polym151532593823199310708145)
Hou, S. H., F. F. Zhou, Y. H. Sun, and Q. Z. Li. Deconstructive and divergent synthesis of bioactive natural products. Molecules. 2023. https://doi.org/10.3390/molecules28176193 . (PMID: 10.3390/molecules281761933820271210779997)
Luu, K. T., E. Kraynov, B. Kuang, P. Vicini, and W. Z. Zhong. Modeling, simulation, and translation framework for the preclinical development of monoclonal antibodies. AAPS J. 2013. https://doi.org/10.1208/s12248-013-9464-8 . (PMID: 10.1208/s12248-013-9464-8234080943675753)
Lanman, B. A., J. R. Allen, J. G. Allen, A. K. Amegadzie, K. S. Ashton, S. K. Booker, J. J. Chen, N. Chen, M. J. Frohn, G. Goodman, D. J. Kopecky, L. Liu, P. Lopez, J. D. Low, V. Ma, A. E. Minatti, T. T. Nguyen, N. Nishimura, A. J. Pickrell, A. B. Reed, Y. Shin, A. C. Siegmund, N. A. Tamayo, C. M. Tegley, M. C. Walton, H. L. Wang, R. P. Wurz, M. Xue, K. C. Yang, P. Achanta, M. D. Bartberger, J. Canon, L. S. Hollis, J. D. McCarter, C. Mohr, K. Rex, A. Y. Saiki, T. San Miguel, L. P. Volak, K. H. Wang, D. A. Whittington, S. G. Zech, J. R. Lipford, and V. J. Cee. Discovery of a covalent inhibitor of KRASG12C (AMG 510) for the treatment of solid tumors. J. Med. Chem. 63:52–65, 2020. https://doi.org/10.1021/ACS.JMEDCHEM.9B01180/SUPPL_FILE/JM9B01180_SI_002.CSV . (PMID: 10.1021/ACS.JMEDCHEM.9B01180/SUPPL_FILE/JM9B01180_SI_002.CSV31820981)
Mateo, J., C. J. Lord, V. Serra, A. Tutt, J. Balmaña, M. Castroviejo-Bermejo, C. Cruz, A. Oaknin, S. B. Kaye, and J. S. De Bono. A decade of clinical development of PARP inhibitors in perspective. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 30:1437–1447, 2019. https://doi.org/10.1093/ANNONC/MDZ192 . (PMID: 10.1093/ANNONC/MDZ192)
Robichaux, J. P., Y. Y. Elamin, Z. Tan, B. W. Carter, S. Zhang, S. Liu, S. Li, T. Chen, A. Poteete, A. Estrada-Bernal, A. T. Le, A. Truini, M. B. Nilsson, H. Sun, E. Roarty, S. B. Goldberg, J. R. Brahmer, M. Altan, C. Lu, V. Papadimitrakopoulou, K. Politi, R. C. Doebele, K. K. Wong, and J. V. Heymach. Mechanisms and clinical activity of an EGFR and HER2 exon 20-selective kinase inhibitor in non-small cell lung cancer. Nat. Med. 24:638–646, 2018. https://doi.org/10.1038/s41591-018-0007-9 . (PMID: 10.1038/s41591-018-0007-9296864245964608)
Huang, M., A. Shen, J. Ding, and M. Geng. Molecularly targeted cancer therapy: some lessons from the past decade. Trends Pharmacol. Sci. 2014. https://doi.org/10.1016/j.tips.2013.11.004 . (PMID: 10.1016/j.tips.2013.11.00424361003)
Zargar, S. M., D. K. Hafshejani, A. Eskandarinia, M. Rafienia, and A. Z. Kharazi. A review of controlled drug delivery systems based on cells and cell membranes. J. Med. Signals Sens. 9:181, 2019. https://doi.org/10.4103/JMSS.JMSS_53_18 . (PMID: 10.4103/JMSS.JMSS_53_18315440586743242)
Kotschy, A., Z. Szlavik, J. Murray, J. Davidson, A. L. Maragno, G. Le Toumelin-Braizat, M. Chanrion, G. L. Kelly, J. N. Gong, D. M. Moujalled, A. Bruno, M. Csekei, A. Paczal, Z. B. Szabo, S. Sipos, G. Radics, A. Proszenyak, B. Balint, L. Ondi, G. Blasko, A. Robertson, A. Surgenor, P. Dokurno, I. Chen, N. Matassova, J. Smith, C. Pedder, C. Graham, A. Studeny, G. Lysiak-Auvity, A. M. Girard, F. Gravé, D. Segal, C. D. Riffkin, G. Pomilio, L. C. A. Galbraith, B. J. Aubrey, M. S. Brennan, M. J. Herold, C. Chang, G. Guasconi, N. Cauquil, F. Melchiore, N. Guigal-Stephan, B. Lockhart, F. Colland, J. A. Hickman, A. W. Roberts, D. C. S. Huang, A. H. Wei, A. Strasser, G. Lessene, and O. Geneste. The MCL1 inhibitor S63845 is tolerable and effective in diverse cancer models. Nature. 538:477–482, 2016. https://doi.org/10.1038/nature19830 . (PMID: 10.1038/nature1983027760111)
Murga, M., and O. Fernandez-Capetillo. Emerging concepts in drug discovery for cancer therapy. Mol. Oncol. 16:3757–3760, 2022. https://doi.org/10.1002/1878-0261.13325 . (PMID: 10.1002/1878-0261.13325363217229627784)
Bryant, H. E., N. Schultz, H. D. Thomas, K. M. Parker, D. Flower, E. Lopez, S. Kyle, M. Meuth, N. J. Curtin, and T. Helleday. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature. 434:913–917, 2005. https://doi.org/10.1038/nature03443 . (PMID: 10.1038/nature0344315829966)
Farmer, H., H. McCabe, C. J. Lord, A. H. J. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. B. Martin, S. P. Jackson, G. C. M. Smith, and A. Ashworth. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 434:917–921, 2005. https://doi.org/10.1038/nature03445 . (PMID: 10.1038/nature0344515829967)
Niggenaber, J., J. Hardick, J. Lategahn, and D. Rauh. Structure defines function: clinically relevant mutations in ErbB kinases. J. Med. Chem. 63:40–51, 2020. https://doi.org/10.1021/acs.jmedchem.9b00964 . (PMID: 10.1021/acs.jmedchem.9b0096431414802)
Melanoma Targeted Therapy | Targeted Drugs for Melanoma | American Cancer Society [WWW Document], n.d.
An, H. W., M. Mamuti, X. Wang, H. Yao, M. . Di. Wang, L. Zhao, and L. L. Li. Rationally designed modular drug delivery platform based on intracellular peptide self-assembly. Exploration. 2021. https://doi.org/10.1002/EXP.20210153 . (PMID: 10.1002/EXP.202101533732321710190849)
Ezike, T. C., U. S. Okpala, U. L. Onoja, C. P. Nwike, E. C. Ezeako, O. J. Okpara, C. C. Okoroafor, S. C. Eze, O. L. Kalu, E. C. Odoh, U. G. Nwadike, J. O. Ogbodo, B. U. Umeh, E. C. Ossai, and B. C. Nwanguma. Advances in drug delivery systems, challenges and future directions. Heliyon. 2023. https://doi.org/10.1016/J.HELIYON.2023.E17488 . (PMID: 10.1016/J.HELIYON.2023.E174883741668010320272)
Pasut, G. Grand challenges in nano-based drug delivery. Front. Med. Technol. 1:501667, 2019. https://doi.org/10.3389/FMEDT.2019.00001/BIBTEX . (PMID: 10.3389/FMEDT.2019.00001/BIBTEX)
Sharma, S., R. Parveen, and B. P. Chatterji. Toxicology of nanoparticles in drug delivery. Curr. Pathobiol. Rep. 9:133–144, 2021. https://doi.org/10.1007/S40139-021-00227-Z/FIGURES/2 . (PMID: 10.1007/S40139-021-00227-Z/FIGURES/2348409188611175)
Youn, Y. S., and Y. H. Bae. Perspectives on the past, present, and future of cancer nanomedicine. Adv. Drug Deliv. Rev. 130:3–11, 2018. https://doi.org/10.1016/J.ADDR.2018.05.008 . (PMID: 10.1016/J.ADDR.2018.05.00829778902)
Mahdavi, M., A. Fattahi, E. Tajkhorshid, and S. Nouranian. Molecular insights into the loading and dynamics of doxorubicin on PEGylated graphene oxide nanocarriers. ACS Appl. Bio Mater. 3:1354–1363, 2020. https://doi.org/10.1021/acsabm.9b00956 . (PMID: 10.1021/acsabm.9b00956333134827731932)
Albano, J. M. R., E. de Paula, and M. Pickholz. Molecular dynamics simulations to study drug delivery systems. Mol Dyn. 2018. https://doi.org/10.5772/intechopen.75748 . (PMID: 10.5772/intechopen.75748)
Pillay, S., V. Pillay, Y. E. Choonara, D. Naidoo, R. A. Khan, L. C. du Toit, V. M. K. Ndesendo, G. Modi, M. P. Danckwerts, and S. E. Iyuke. Design, biometric simulation and optimization of a nano-enabled scaffold device for enhanced delivery of dopamine to the brain. Int. J. Pharm. 382:277–290, 2009. https://doi.org/10.1016/j.ijpharm.2009.08.021 . (PMID: 10.1016/j.ijpharm.2009.08.02119703530)
Arabian, T., S. Amjad-Iranagh, and R. Halladj. Molecular dynamics simulation study of doxorubicin adsorption on functionalized carbon nanotubes with folic acid and tryptophan. Sci. Rep. 11:1–11, 2021. https://doi.org/10.1038/s41598-021-03619-8 . (PMID: 10.1038/s41598-021-03619-8)
Liu, Q., Z. Xue, and D. Xu. Molecular dynamics characterization of Sr-doped biomimetic hydroxyapatite nanoparticles. J. Phys. Chem. C. 124:19704–19715, 2020. https://doi.org/10.1021/acs.jpcc.0c06391 . (PMID: 10.1021/acs.jpcc.0c06391)
Kuhlman, B., and P. Bradley. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 2019. https://doi.org/10.1038/s41580-019-0163-x . (PMID: 10.1038/s41580-019-0163-x314171967032036)
Salo-Ahen, O. M. H., I. Alanko, R. Bhadane, A. M. Alexandre, R. V. Honorato, S. Hossain, A. H. Juffer, A. Kabedev, M. Lahtela-Kakkonen, A. S. Larsen, E. Lescrinier, P. Marimuthu, M. U. Mirza, G. Mustafa, A. Nunes-Alves, T. Pantsar, A. Saadabadi, K. Singaravelu, and M. Vanmeert. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 2021. https://doi.org/10.3390/pr9010071 . (PMID: 10.3390/pr9010071)
Hasanzade, Z., and H. Raissi. Solvent/co-solvent effects on the electronic properties and adsorption mechanism of anticancer drug Thioguanine on Graphene oxide surface as a nanocarrier: density functional theory investigation and a molecular dynamics. Appl. Surf. Sci. 2017. https://doi.org/10.1016/j.apsusc.2017.05.245 . (PMID: 10.1016/j.apsusc.2017.05.245)
Mehta, M., T. A. Bui, X. Yang, Y. Aksoy, E. M. Goldys, and W. Deng. Lipid-based nanoparticles for drug/gene delivery: an overview of the production techniques and difficulties encountered in their industrial development. ACS Mater. Au. 2023. https://doi.org/10.1021/acsmaterialsau.3c00032 . (PMID: 10.1021/acsmaterialsau.3c000323808966610636777)
Liao, S., L. Wei, A. E. Bouchez, and F. Stellacci. Influence of structural dynamics on cell uptake investigated with single-chain polymeric nanoparticles. Chem. 2023. https://doi.org/10.1016/j.chempr.2023.03.012 . (PMID: 10.1016/j.chempr.2023.03.012)
Abedi-Gaballu, F., G. Dehghan, M. Ghaffari, R. Yekta, S. Abbaspour-Ravasjani, B. Baradaran, J. Ezzati Nazhad Dolatabadi, and M. R. Hamblin. PAMAM dendrimers as efficient drug and gene delivery nanosystems for cancer therapy. Appl. Mater. Today. 2018. https://doi.org/10.1016/j.apmt.2018.05.002 . (PMID: 10.1016/j.apmt.2018.05.002305110146269116)
Daniyal, M., B. Liu, and W. Wang. Comprehensive review on graphene oxide for use in drug delivery system. Curr. Med. Chem. 2020. https://doi.org/10.2174/13816128256661902011296290 . (PMID: 10.2174/1381612825666190201129629030767730)
He, S., L. Wu, X. Li, H. Sun, T. Xiong, J. Liu, C. Huang, H. Xu, H. Sun, W. Chen, R. Gref, and J. Zhang. Metal-organic frameworks for advanced drug delivery. Acta Pharm. Sin. B. 2021. https://doi.org/10.1016/j.apsb.2021.03.019 . (PMID: 10.1016/j.apsb.2021.03.019358475189136566)
Chen, D., X. Liu, X. Lu, and J. Tian. Nanoparticle drug delivery systems for synergistic delivery of tumor therapy. Front. Pharmacol. 2023. https://doi.org/10.3389/fphar.2023.1111991 . (PMID: 10.3389/fphar.2023.11119913863833410777313)
Zhang, M., J. Zhu, Y. Zheng, R. Guo, S. Wang, S. Mignani, A. M. Caminade, J. P. Majoral, and X. Shi. Doxorubicin-conjugated PAMAM dendrimers for pH-responsive drug release and folic acid-targeted cancer therapy. Pharmaceutics. 2018. https://doi.org/10.3390/pharmaceutics10030162 . (PMID: 10.3390/pharmaceutics10030162305836016359657)
Liu, J., Y. Zhang, C. Liu, Y. Jiang, Z. Wang, and X. Li. Paclitaxel prodrug-encapsulated polypeptide micelles with redox/pH dual responsiveness for cancer chemotherapy. Int. J. Pharm. 2023. https://doi.org/10.1016/j.ijpharm.2023.123398 . (PMID: 10.1016/j.ijpharm.2023.1233983816099011124712)
Pourmadadi, M., M. M. Eshaghi, E. Rahmani, N. Ajalli, S. Bakhshi, H. Mirkhaef, M. V. Lasemi, A. Rahdar, R. Behzadmehr, and A. M. Díez-Pascual. Cisplatin-loaded nanoformulations for cancer therapy: a comprehensive review. J. Drug Deliv. Sci. Technol. 2022. https://doi.org/10.1016/j.jddst.2022.103928 . (PMID: 10.1016/j.jddst.2022.103928)
Ganthala, P. D., S. Alavala, N. Chella, S. B. Andugulapati, N. B. Bathini, and R. Sistla. Co-encapsulated nanoparticles of Erlotinib and Quercetin for targeting lung cancer through nuclear EGFR and PI3K/AKT inhibition. Colloids Surfaces B Biointerfaces. 2022. https://doi.org/10.1016/j.colsurfb.2021.112305 . (PMID: 10.1016/j.colsurfb.2021.11230534998178)
Khademi, R., Z. Mohammadi, R. Khademi, A. Saghazadeh, and N. Rezaei. Nanotechnology-based diagnostics and therapeutics in acute lymphoblastic leukemia: a systematic review of preclinical studies. Nanoscale Adv. 2023. https://doi.org/10.1039/d2na00483f . (PMID: 10.1039/d2na00483f367565029890594)
Wang, L., M. Chen, X. Ran, H. Tang, and D. Cao. Sorafenib-based drug delivery systems: applications and perspectives. Polymers (Basel). 2023. https://doi.org/10.3390/polym15122638 . (PMID: 10.3390/polym151226383823203010781181)
Zhu, L., Q. Mu, J. Yu, J. I. Griffin, X. Xu, and R. J. Y. Ho. ICAM-1 targeted drug combination nanoparticles enhanced gemcitabine-paclitaxel exposure and breast cancer suppression in mouse models. Pharmaceutics. 2022. https://doi.org/10.3390/pharmaceutics14010089 . (PMID: 10.3390/pharmaceutics14010089366787699865350)
Parvez, A., F. Choudhary, P. Mudgal, R. Khan, K. A. Qureshi, H. Farooqi, and A. Aspatwar. PD-1 and PD-L1: architects of immune symphony and immunotherapy breakthroughs in cancer treatment. Front. Immunol. 2023. https://doi.org/10.3389/fimmu.2023.1296341 . (PMID: 10.3389/fimmu.2023.12963413810641510722272)
Abdi, F., E. Arkan, K. Mansouri, Z. Shekarbeygi, and E. Barzegari. Interactions of Bevacizumab with chitosan biopolymer nanoparticles: Molecular modeling and spectroscopic study. J. Mol. Liq. 2021. https://doi.org/10.1016/j.molliq.2021.116655 . (PMID: 10.1016/j.molliq.2021.116655)
Suzuki, Y., and H. Ishihara. Difference in the lipid nanoparticle technology employed in three approved siRNA (Patisiran) and mRNA (COVID-19 vaccine) drugs. Drug Metab. Pharmacokinet. 2021. https://doi.org/10.1016/j.dmpk.2021.100424 . (PMID: 10.1016/j.dmpk.2021.100424347572878502116)
Yonezawa, S., H. Koide, and T. Asai. Recent advances in siRNA delivery mediated by lipid-based nanoparticles. Adv. Drug Deliv. Rev. 2020. https://doi.org/10.1016/j.addr.2020.07.022 . (PMID: 10.1016/j.addr.2020.07.022327685647406478)
Yeo, S., M. J. Kim, Y. K. Shim, I. Yoon, and W. K. Lee. Solid lipid nanoparticles of curcumin designed for enhanced bioavailability and anticancer efficiency. ACS Omega. 2022. https://doi.org/10.1021/acsomega.2c04407 . (PMID: 10.1021/acsomega.2c04407362493829558702)
Hosseini Berenji, R., A. Pezeshki, B. Ghanbarzadeh, M. Mohammadi, M. Tabibi Azar, H. Hamishehkar, F. Ahmadzadeh Nobari Azar, and M. Ghorbani. Resveratrol entrapped food grade lipid nanocarriers as a potential antioxidant in a mayonnaise. Food Biosci. 2021. https://doi.org/10.1016/j.fbio.2021.101041 . (PMID: 10.1016/j.fbio.2021.101041)
Jain, K. K. An overview of drug delivery systems. Methods Mol Biol. 2020. https://doi.org/10.1007/978-1-4939-9798-5_1 . (PMID: 10.1007/978-1-4939-9798-5_131893451)
Lomzov, A. A., Y. N. Vorobjev, and D. V. Pyshnyi. Evaluation of the Gibbs free energy changes and melting temperatures of DNA/DNA duplexes using hybridization enthalpy calculated by molecular dynamics simulation. J. Phys. Chem. B. 2015. https://doi.org/10.1021/acs.jpcb.5b09645 . (PMID: 10.1021/acs.jpcb.5b09645265691474824051)
Zhu, S., N. Lempesis, P. J. In ‘T Veld, and G. C. Rutledge. Molecular simulation of thermoplastic polyurethanes under large tensile deformation. Macromolecules. 2018. https://doi.org/10.1021/acs.macromol.7b02367 . (PMID: 10.1021/acs.macromol.7b02367315379476752221)
Schichtel, J. J., and A. Chattopadhyay. Modeling thermoset polymers using an improved molecular dynamics crosslinking methodology. Comput. Mater. Sci. 2020. https://doi.org/10.1016/j.commatsci.2019.109469 . (PMID: 10.1016/j.commatsci.2019.109469)
Chen, Q., Z. Zhang, Y. Huang, H. Zhao, Z. Chen, K. Gao, T. Yue, L. Zhang, and J. Liu. Structure−mechanics relation of natural rubber: insights from molecular dynamics simulations. ACS Appl. Polym. Mater. 2022. https://doi.org/10.1021/acsapm.2c00147 . (PMID: 10.1021/acsapm.2c00147)
Shen, K. H., M. Fan, and L. M. Hall. Molecular dynamics simulations of ion-containing polymers using generic coarse-grained models. Macromolecules. 2021. https://doi.org/10.1021/acs.macromol.0c02557 . (PMID: 10.1021/acs.macromol.0c02557)
Siani, P., E. Donadoni, L. Ferraro, F. Re, and C. Di Valentin. Molecular dynamics simulations of doxorubicin in sphingomyelin-based lipid membranes. Biochim. Biophys. Acta—Biomembr. 2022. https://doi.org/10.1016/j.bbamem.2021.183763 . (PMID: 10.1016/j.bbamem.2021.18376334506799)
Kharazian, B., A. A. Ahmad, and A. Mabudi. A molecular dynamics study on the binding of gemcitabine to human serum albumin. J. Mol. Liq. 337:116496, 2021. https://doi.org/10.1016/J.MOLLIQ.2021.116496 . (PMID: 10.1016/J.MOLLIQ.2021.116496)
Lim, J., S. T. Lo, S. Hill, G. M. Pavan, X. Sun, and E. E. Simanek. Antitumor activity and molecular dynamics simulations of paclitaxel-laden triazine dendrimers. Mol. Pharm. 9:404, 2012. https://doi.org/10.1021/MP2005017 . (PMID: 10.1021/MP2005017222603287768605)
Dong-Dong Li, Ting-Ting Wu, Pan Yu, Zhen-Zhong Wang, Wei Xiao, Yan Jiang, Lin-Guo Zhao. Molecular Dynamics Analysis of Binding Sites of Epidermal Growth Factor Receptor Kinase Inhibitors. ACS Omega. 5:26, 2020. https://doi.org/10.1021/acsomega.0c02183 .
Owen, A. E., C. M. Chima, I. Ahmad, W. Emori, E. C. Agwamba, C. R. Cheng, I. Benjamin, H. Patel, E. F. Ahuekwe, M. A. Ojong, C. B. Ubah, A. L. E. Manicum, and H. Louis. Antibacterial potential of trihydroxycyclohexa-2,4-diene-1-carboxylic acid: insight from DFT, molecular docking, and molecular dynamic simulation. Polycycl. Aromat. Compd. 2024. https://doi.org/10.1080/10406638.2023.2214280 . (PMID: 10.1080/10406638.2023.2214280)
Jiao, Y., C. Shi, and Y. Sun. Unraveling the Role of scutellaria baicalensis for the treatment of breast cancer using network pharmacology, molecular docking, and molecular dynamics simulation. Int. J. Mol. Sci. 2023. https://doi.org/10.3390/ijms24043594 . (PMID: 10.3390/ijms240435943820343310779386)
Isa, M. A., and A. P. Kappo. Exploring phytoconstituents through molecular dynamics simulation: uncovering potential inhibitors for multiple targeted pathways in breast cancer. J. Proteins Proteomics. 16:125–140, 2025. https://doi.org/10.1007/s42485-025-00176-w . (PMID: 10.1007/s42485-025-00176-w)
Rashidieh, B., M. Valizadeh, V. Assadollahi, and M. M. Ranjbar. Molecular dynamics simulation on the low sensitivity of mutants of NEDD-8 activating enzyme for MLN4924 inhibitor as a cancer drug. Am. J. Cancer Res. 5:3400–3406, 2015. (PMID: 268073204697686)
Johnson-Arbor, K., and R. Dubey. Doxorubicin. xPharm Compr. Pharmacol. Ref. 2023. https://doi.org/10.1016/B978-008055232-3.61650-2 . (PMID: 10.1016/B978-008055232-3.61650-2)
Kotzabasaki, M., I. Galdadas, E. Tylianakis, E. Klontzas, Z. Cournia, and G. E. Froudakis. Multiscale simulations reveal IRMOF-74-III as a potent drug carrier for gemcitabine delivery. J. Mater. Chem. B. 5:3277–3282, 2017. https://doi.org/10.1039/c7tb00220c . (PMID: 10.1039/c7tb00220c32264393)
Wang, X. Y., L. Zhang, X. H. Wei, and Q. Wang. Molecular dynamics of paclitaxel encapsulated by salicylic acid-grafted chitosan oligosaccharide aggregates. Biomaterials. 34:1843–1851, 2013. https://doi.org/10.1016/J.BIOMATERIALS.2012.11.024 . (PMID: 10.1016/J.BIOMATERIALS.2012.11.02423219327)
Azizi, A., and S. Ebrahimi. Molecular dynamics study of PTX adsorption onto n-doped graphene in vacuum and aqueous environments. Nano. 9:1, 2015. https://doi.org/10.1142/S179329201450088X . (PMID: 10.1142/S179329201450088X)
del De Lama-Odría, M. C., L. J. del Valle, and J. Puiggalí. Hydroxyapatite biobased materials for treatment and diagnosis of cancer. Int. J. Mol. Sci. 23:11352, 2022. https://doi.org/10.3390/ijms231911352 . (PMID: 10.3390/ijms231911352362326529569977)
Zhu, C., X. Zhou, Z. Liu, H. Chen, H. Wu, X. Yang, X. Zhu, J. Ma, and H. Dong. The morphology of hydroxyapatite nanoparticles regulates cargo recognition in clathrin-mediated endocytosis. Front. Mol. Biosci. 2021. https://doi.org/10.3389/FMOLB.2021.627015/FULL . (PMID: 10.3389/FMOLB.2021.627015/FULL352656668740137)
Hu, J., Zhou, L., Jiang, J., 2023. Efficient machine learning force field for large-scale molecular simulations of organic systems. https://doi.org/10.31635/ccschem.024.202404785.
Mollahosseini, A., and A. Abdelrasoul. Molecular dynamics simulation for membrane separation and porous materials: a current state of art review. J. Mol. Graph. Model. 2021. https://doi.org/10.1016/J.JMGM.2021.107947 . (PMID: 10.1016/J.JMGM.2021.10794734126546)
Kuhlman, B., and P. Bradley. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20:681–697, 2019. https://doi.org/10.1038/s41580-019-0163-x . (PMID: 10.1038/s41580-019-0163-x314171967032036)
Zare, H., S. Ahmadi, A. Ghasemi, M. Ghanbari, N. Rabiee, M. Bagherzadeh, M. Karimi, T. J. Webster, M. R. Hamblin, and E. Mostafavi. Carbon nanotubes: smart drug/gene delivery carriers. Int. J. Nanomedicine. 2021. https://doi.org/10.2147/IJN.S299448 . (PMID: 10.2147/IJN.S299448336881857936533)
Perilla, J. R., B. C. Goh, C. K. Cassidy, B. Liu, R. C. Bernardi, T. Rudack, H. Yu, Z. Wu, and K. Schulten. Molecular dynamics simulations of large macromolecular complexes. Curr. Opin. Struct. Biol. 31:64–74, 2015. https://doi.org/10.1016/J.SBI.2015.03.007 . (PMID: 10.1016/J.SBI.2015.03.007258457704476923)
Noé, F., A. Tkatchenko, K. R. Müller, and C. Clementi. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–390, 2020. https://doi.org/10.1146/annurev-physchem-042018-052331 . (PMID: 10.1146/annurev-physchem-042018-05233132092281)
Lei, H., X. Li, J. Wang, Y. Song, G. Tian, M. Huang, and D. Wu. DFT and molecular dynamic simulation for the dielectric property analysis of polyimides. Chem. Phys. Lett. 2022. https://doi.org/10.1002/jcc.21787 . (PMID: 10.1016/j.cplett.2021.139131)
Michaud-Agrawal, N., E. J. Denning, T. B. Woolf, and O. Beckstein. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32(10):2319–2327, 2011. https://doi.org/10.1002/jcc.v32.10.1002/jcc.21787 .
Pronk, S., S. Páll, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J. C. Smith, P. M. Kasson, D. van der Spoel, B. Hess, and E. Lindahl. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 29(7) 845–854, 2013. https://doi.org/10.1093/bioinformatics/btt055 .
Zhang, H., W. Liao, G. Chen, and H. Ma. Development and characterization of coal-based thermoplastic composite material for sustainable construction. Sustainability. 15(16):12446, 2023. https://doi.org/10.3390/su151612446 .
Zhang, Z. W., and W. C. Lu. AmberMDrun: a scripting tool for running Amber MD in an easy way. Biomolecules. 13(4):635, 2023. https://doi.org/10.3390/biom13040635 .
Thompson, A. P., H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton (2022) LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic meso and continuum scales. Comput. Phys. Commun. 271108171. https://doi.org/10.1016/j.cpc.2021.108171 . (PMID: 10.1016/j.cpc.2021.108171)
Phillips, J. C., D. J. Hardy, J. D. C. Maia, J. E. Stone, J. V. Ribeiro, R. C. Bernardi, R. Buch, G. Fiorin, J. Hénin, W. Jiang, R. McGreevy, M. C. R. Melo, B. K. Radak, R. D. Skeel, A. Singharoy, Y. Wang, B. Roux, A. Aksimentiev, Z. Luthey-Schulten, L. V. Kalé, K. Schulten, C. Chipot, E. Tajkhorshid. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 153(4), 2020. https://doi.org/10.1063/5.0014475 .
Contributed Indexing:
Keywords: Cancer treatment; Computational biology; Drug delivery; Molecular dynamics; Nanocarriers
Substance Nomenclature:
0 (Antineoplastic Agents)
0 (Nanotubes, Carbon)
0 (Drug Carriers)
9012-76-4 (Chitosan)
0 (Nanotubes, Carbon)
0 (Drug Carriers)
9012-76-4 (Chitosan)
Entry Date(s):
Date Created: 20250930 Date Completed: 20251208 Latest Revision: 20251208
Update Code:
20251208
DOI:
10.1007/s10439-025-03864-2
PMID:
41023346
Database:
MEDLINE
Weitere Informationen
Molecular Dynamics (MD) simulations have emerged as a vital tool in optimizing drug delivery for cancer therapy, offering detailed atomic-level insights into the interactions between drugs and their carriers. Unlike traditional experimental methods, which can be resource-intensive and time-consuming, MD simulations provide a more efficient and precise approach to studying drug encapsulation, stability, and release processes. These simulations are essential for designing effective drug carriers and gaining a deeper understanding of the molecular mechanisms that influence drug behavior in biological systems. Recent research has highlighted the broad applicability of MD simulations in assessing different drug delivery systems, such as functionalized carbon nanotubes (FCNTs), chitosan-based nanoparticles, metal-organic frameworks (MOFs), and human serum albumin (HSA). FCNTs are known for their high drug-loading capacity and stability, while biocompatible carriers like HSA and chitosan are favored for their biodegradability and reduced toxicity. Case studies involving anticancer drugs, including Doxorubicin (DOX), Gemcitabine (GEM), and Paclitaxel (PTX), showcase how MD simulations can improve drug solubility and optimize controlled release mechanisms. Although the computational complexity of these simulations presents challenges, advances in high-performance computing and machine learning techniques are driving significant progress. These innovations are facilitating the development of more targeted and efficient cancer therapies. By combining MD simulations with experimental validation, researchers are enhancing predictive models and accelerating the creation of next-generation drug delivery systems.
(© 2025. The Author(s) under exclusive licence to Biomedical Engineering Society.)
Declarations. Competing interests: The authors declare no competing interests.